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Human Brain Mapping 38:1140–1154 (2017)
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Tonotopic Maps in Human Auditory Cortex
Using Arterial Spin Labeling
Anna Gardumi,* Dimo Ivanov, Martin Havlicek, Elia Formisano, and
K^
amil Uluda
g
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht
University, Maastricht, The Netherlands
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Abstract: A tonotopic organization of the human auditory cortex (AC) has been reliably found by neuroimaging studies. However, a full characterization and parcellation of the AC is still lacking. In this
study, we employed pseudo-continuous arterial spin labeling (pCASL) to map tonotopy and voice
selective regions using, for the first time, cerebral blood flow (CBF). We demonstrated the feasibility of
CBF-based tonotopy and found a good agreement with BOLD signal-based tonotopy, despite the lower
contrast-to-noise ratio of CBF. Quantitative perfusion mapping of baseline CBF showed a region of
high perfusion centered on Heschl’s gyrus and corresponding to the main high-low-high frequency
gradients, co-located to the presumed primary auditory core and suggesting baseline CBF as a novel
marker for AC parcellation. Furthermore, susceptibility weighted imaging was employed to investigate
the tissue specificity of CBF and BOLD signal and the possible venous bias of BOLD-based tonotopy.
For BOLD only active voxels, we found a higher percentage of vein contamination than for CBF only
active voxels. Taken together, we demonstrated that both baseline and stimulus-induced CBF is an
alternative fMRI approach to the standard BOLD signal to study auditory processing and delineate the
C 2016 The Authors
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functional organization of the auditory cortex. Hum Brain Mapp 38:1140–1154, 2017.
Human Brain Mapping Published by Wiley Periodicals, Inc.
Key words: tonotopy; fMRI; ASL; CBF; quantitative perfusion; SWI; primary auditory cortex
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INTRODUCTION
Additional Supporting Information may be found in the online
version of this article.
Contract grant sponsor: Marie Curie Initial Training Network
grant of EU; Contract grant number: PITN-GA-2009-238593; Contract grant sponsor: VIDI grant of the Netherlands Organization
for Scientific Research (NWO); Contract grant number: KU, 45211-002; Contract grant sponsor: NWO VICI; Contract grant number: 453-12-002
*Correspondence to: Anna Gardumi, Department of Cognitive
Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, PO Box 616, 6200MD Maastricht, The Netherlands.
E-mail: [email protected]
Received for publication 31 July 2016; Revised 27 September 2016;
Accepted 11 October 2016.
DOI: 10.1002/hbm.23444
Published online 28 October 2016 in Wiley Online Library
(wileyonlinelibrary.com).
Neuroimaging techniques allow for the non-invasive
investigation of the functional organization of the auditory
cortex (AC) in the human brain: In agreement with previous animal studies (e.g., cats and primates; Merzenich and
Brugge, 1973; Merzenich et al., 1973), functional magnetic
resonance imaging (fMRI) studies have found a tonotopic
organization of the human early AC (Da Costa et al., 2011;
Formisano et al., 2003; Humphries et al., 2010; Langers
and van Dijk, 2012; Talavage et al., 2004; Woods et al.,
2010). Human early AC tonotopy has been consistently
described as two frequency gradients composing a highlow-high preferred frequency pattern located in and
around the Heschl’s gyrus (HG). However, its extent, orientation, and finer details are still a matter of debate. In
the visual cortex, the early visual areas are parcellated by
means of stimuli varying within the two dimensional
C 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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Tonotopic Maps in Auditory Cortex Using ASL
extent of the visual field, called retinotopy. In contrast, in
the auditory domain, a second stimulus dimension is so
far missing that would enable delineation of the borders of
early auditory areas.
Within the high-low-high frequency map, the more posterior/medial frequency gradient is considered the human
homolog of monkey area A1, while the more anterior/lateral frequency gradient is considered the human homolog
of monkey area R. These two areas (gradients) together
are considered to define the human primary auditory cortex
(PAC, also called auditory core) (Baumann et al., 2013,
Moerel et al., 2014; Saenz and Langers, 2014). However,
the exact extent of the PAC remains ambiguous: Some
studies consider the main high-low-high frequency gradients to be entirely included in the PAC (Da costa et al.,
2011), while other studies attribute part of them to auditory belt regions (Humphries et al., 2010; Talavage et al.,
2004). Moreover, three main theories about the orientation
of the PAC coexist: The classical interpretation locates the
PAC along HG and finds its foundations in cytoarchitectonic studies. The orthogonal interpretation locates the
PAC across HG assuming in between-species homology
with monkeys (for which PAC was shown to run parallel
to the superior temporal gyrus, STG). The oblique interpretation is a more recent theory proposing an oblique orientation of PAC with respect to HG (Baumann et al.,
2013). Finally, additional tonotopic gradients outside of the
HG are often observed, especially at high spatial resolution and single-subject level (Da Costa et al., 2011; Herdener et al., 2013; Moerel et al., 2013).
Besides tonotopy, other functional organizations of AC
related to sound properties have been investigated, such
Abbreviations
AC
ASL
BOLD
CBA
CBF
CBV
CNR
COV
EPI
GLM
GM
HG
HRF
MTG
PAC
PLD
PP
PT
SNR
STG
STP
STS
SWI
human auditory cortex
arterial spin labeling
blood oxygenation level-dependent
cortex-based alignment
cerebral blood flow
cerebral blood volume
contrast-to-noise ratio
coefficient-of-variation
echo-planar imaging
general linear model
gray matter
Heschl’s gyrus
hemodynamic response function
middle temporal gyrus
primary auditory cortex
post-labeling delay
planum polare
planum temporale
signal-to-noise ratio
superior temporal gyrus
superior temporal plane
superior temporal sulcus
susceptibility weighted imaging
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as tuning width and periodicity preference (Barton et al.,
2012; Herdener et al., 2013; Moerel et al., 2012, 2013).
Moreover, also higher order functional areas have been
studied: Specifically, regions showing differential response
for vocalizations with respect to other sound categories
(tones, objects, environmental noises, etc.) have been reliably observed and are typically referred to as voice sensitive
areas (Belin et al., 2000).
In addition to tonotopic gradients, anatomical landmarks are utilized to identify PAC. Recently, “myelin”
imaging using MRI has been proposed for that purpose
(De Martino et al., 2015). Primary sensory areas, such as
early visual, somatosensory and auditory cortex, have
been hypothesized to have higher myelin content than the
surrounding brain areas, and they are detectable with T1
and T2* MRI contrasts (Bock et al., 2009; Cohen-Adad
et al., 2012; De Martino et al., 2015; Dick et al., 2012; Geyer
et al., 2011; Glasser and Van Essen, 2011; Sereno et al.,
2013; Sigalovsky et al., 2006). Alternatively, susceptibility
weighted imaging (SWI) can be used to probe iron and
myelin differences in the cortex and, additionally, to locate
veins. SWI is a recent MR technique detecting susceptibility differences in the brain (Haacke et al., 2004; Reichenbach et al., 1997). SWI combines magnitude and phase
information of the complex T2* weighted images to
enhance the contrast of paramagnetic substances, such as
deoxygenated hemoglobin and iron, with respect to the
surrounding diamagnetic tissue.
FMRI studies investigating the functional organization
of AC have so far employed the blood oxygenation leveldependent (BOLD) effect as an indirect measure of neural
activity. Although the BOLD signal is the standard contrast for fMRI, it has some limitations in terms of spatial
specificity and of being a quantitative marker of neuronal
activity. The BOLD signal arises from the combined
changes of oxygen metabolism (CMRO2), cerebral blood
flow (CBF), and cerebral blood volume (CBV) in response
to neural activity modulations (Logothetis, 2008). It has
been shown that the overall changes in the oxygenation
spread from the location of the neural activity into the
draining veins. At low magnetic field strength (e.g., 1.5
and 3 T) and for both gradient-echo and spin-echo sequences, the measured BOLD signal mostly originates from
draining veins (Uludag et al., 2009). Consequently, the
spatial specificity of the BOLD signal is biased by the presence of draining veins causing signal blurring and possible
displacement from the actual site of neural activity (Ugurbil et al., 2003).
Besides the BOLD signal, alternative fMRI acquisition
techniques exist to study brain function. For example, arterial spin labeling (ASL) techniques measure absolute CBF
in addition to the BOLD signal. ASL allows to quantify
both stimulation-induced and baseline CBF as an absolute
marker of the physiological state of the tissue and its
changes. Studies have shown that CBF, compared to the
BOLD signal, is more spatially localized to neural activity,
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has lower intersubject variability and is more reproducible
over time (Aguirre et al., 2002; Wang et al., 2003; Tjandra
et al., 2005). However, CBF signal has a lower SNR and its
quantitative estimation is challenged by MRI acquisition
confounds such as transit delay, magnetization transfer
and relaxation time effects. Nevertheless, the post-labeling
delay (PLD) interval (i.e., the time interval occurring
between blood labeling and image acquisition, which is
necessary for the labeled blood to flow from the tagging
location to the imaging slab) represents an inherently
silent gap in the ASL MR sequence, allowing therefore
auditory stimulation in the absence of scanner noise and
making ASL particularly attractive for auditory fMRI
studies.
In this paper, we aim to assess – for the first time – the
feasibility of tonotopic mapping of the human auditory
cortex with ASL fMRI, and to compare BOLD and CBF
based tonotopies. Additionally, we evaluate whether baseline quantitative CBF and SWI provide information on the
location of the PAC in addition to anatomical landmarks
and myelination.
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In one set of experiments (subjects nr 1 to 6), stimulus
conditions were presented in blocks of six TRs with one
sound per TR (5 3s) (same as in De Martino et al., 2013).
Each stimulus block was followed by a resting block of six
TRs with no auditory stimulation. In another set of experiments (subjects nr 7 to 12), the stimulus duration was
reduced to four TRs and the rest condition increased to
eight TRs. Stimulus conditions were presented in randomized order and repeated twice per run. Six runs were
acquired for each subject, for a total of 96 stimulus blocks.
An additional voice localizer run was acquired (except
for subject 1) using the same block design as above. For
this run, however, the auditory stimuli used (adapted
from Bonte et al., 2013) were 1.0 s long and consisted of
vocal sounds (both speech and non-speech), other natural
sounds (musical instruments, environmental and tool
noises, and animal cries), or amplitude-modulated tones
(8Hz, frequency between 0.3 and 3.0 kHz). The run included eight blocks of each of these three categories for a
total of 304 TRs and 15 min duration.
During all functional runs, subjects were asked to fixate
a cross in the center of the screen and passively listen to
the sounds.
MATERIAL AND METHODS
Subjects
MRI Acquisition
Twelve healthy volunteers (six females, age range 25–33)
with normal hearing took part in this experiment. Written
informed consent was obtained from all participants
according to the approval of the study protocol by the Ethical Committee of the Faculty of Psychology and Neuroscience, Maastricht University.
Measurements were performed on a 3T Prisma Siemens
scanner using a 64-channel head coil. Functional runs
were acquired using pseudo-continuous ASL (pCASL)
with 2D single-shot echo-planar imaging (EPI) readout
(TR 3s, TE 13ms, voxel size 2.5mm isotropic, 19 slices,
labeling duration 1.2s, post labeling delay 1.2s, partial
Fourier 7/8, GRAPPA 2; Dai et al., 2008). Labeling duration and PLD were deliberately chosen shorter than recommended by the ASL white paper (Alsop et al., 2015),
whose recommendations are tailored in view of whole
brain baseline perfusion measures. These choices allow
us shorter TR and thus improved sampling of the hemodynamic response. At the same time, PLD was chosen
long enough to allow CBF quantification in the auditory
cortex, which is adequately perfused by the labeled blood
after a PLD of 1.0 s (Donahue et al., 2014; Mezue et al.,
2014).
This sequence is acoustically characterized by a tagging
module and EPI-train presenting a power spectrum with
contribution mostly from frequencies below 3–4 kHz (peak
frequencies were 990.5, 1981.0, and 2993.0 Hz for the tagging module and 925.9, 1163.0, 1809.0, and 2713.0 Hz for
the EPI-train; see Fig. 1) and relative loudness (estimated
as the ratio between the RMS values of the tagging module and the EPI readout waveforms) of 0.57. Functional
images were acquired while participants passively listened
to the tones presented in a block design. During the stimulus blocks, the sounds were presented in the 1.2s PLD
interval (see Fig. 1A). At the beginning of the fMRI session, the stimuli were subjectively equalized for loudness,
Stimulus Design and Presentation
The stimulus design employed in this work (if not stated otherwise) was adapted from the study by De Martino
et al. (2013; cfr Experiment 3), which investigated tonotopy
in the inferior colliculus and auditory cortex using BOLD
fMRI. The stimuli consisted of amplitude modulated (8Hz,
modulation depth 0.95, length 0.8s, sample rate 44.1 kHz)
tones created in MATLAB with eight carrier frequencies
logarithmically spaced between 0.180 and 7.091 kHz (see
Fig. 1B). Each of these eight frequencies represents one
stimulus condition and, for the rest of the paper, we will
refer to them as center frequencies. In order to introduce
variability within each stimulus condition, for each center
frequency two additional sounds were created with a frequency difference of 610% in logarithmic scale. A total of
24 tones were therefore created (center frequencies are
highlighted in bold): 168,180,193, 284,304,326, 480,514,551,
811,869,931, 1370,1469,1574, 2316,2482,2661, 3915,4196,4497,
6616,7091,7600 Hz. Sound onset and offset were ramped
with a 10ms linear slope and the sound energy (calculated
as root mean square) was equalized.
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Figure 1.
Sound waveform (A) and frequency spectrum (B) of pCASL
with 2D single-shot EPI acquisition. In Panel A, the different
components of a pCASL TR are illustrated: the tagging module,
the silent gap (PLD; in which a stimulus sound may occur following the experimental design), and then the EPI-train readout. In
Panel B, the gray and black lines show the frequency spectrum
of the tagging module and the EPI-train readout, respectively.
The vertical bars indicate the central frequencies of the stimuli
and are color-coded in the same manner as the tonotopic maps
(i.e., from red-to-green-to-blue for low-to-medium-to-high frequency). [Color figure can be viewed at wileyonlinelibrary.com]
and the overall volume of the stimuli was adjusted to a
comfortable intensity level. The stimuli were presented via
MR-compatible earphones (Sensimetrics S14, Malden, MA,
USA). After the experiment, all subjects reported a clear
hearing of the stimuli.
In order to allow absolute quantification of CBF (see
below), an M0 image was acquired using the same pCASL
sequence as described above but with the TR value
increased to 20s.
A susceptibility weighted image was acquired with an inplane resolution of 0.5 3 0.5mm2 and a slice thickness of
1.0mm (TR 28ms, TE 20ms, Flip angle 15deg, GRAPPA 2,
matrix size 384 3 312 3 52; Haacke et al., 2004; Reichenbach
et al., 1997). Finally, a high-resolution (1.0mm isotropic) anatomical image was acquired using an MPRAGE sequence
(TR 2.4s, TE 2.18ms, TI 1040ms, Flip angle 8deg, GRAPPA
2, matrix size 224 3 224 3 192; Mugler and Brookeman,
1990).
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Preprocessing
Functional data were pre-processed in BrainVoyager QX
(Version 2.8.4.2645, 64-bit, Brain Innovation, Maastricht,
The Netherlands): The functional runs were motioncorrected and realigned to the first volume of the fourth
functional run (i.e., the image approximately at the midpoint of the session and closest in terms of acquisition
order to the SWI image, which was acquired between the
third and fourth functional run). 3D motion correction was
performed using the “Trilinear/sinc interpolation” option,
i.e., trilinear interpolation is used during the motion detection step and sinc interpolation for the actual motion correction (spatial transformation) step. Functional ASL, M0
and SWI image were coregistered to the anatomical image
using a gradient-based alignment with six-parameter (i.e.,
three translation and three rotation parameters) rigid body
transformation.
The anatomical image was transformed into Talairach
space in order to employ the automatic segmentation
implemented in BrainVoyager. The results of the automatic segmentation were visually inspected and manually corrected, whenever necessary. The obtained white-gray
matter boundary was reconstructed to produce a 3D
folded cortical surface for each subject. This 3D cortical
representation was used for group alignment purposes to
the aim of both computation and visualization (after inflation) of group maps. The group cortical surfaces were produced taking into account the individual subject’s cortical
curvature via the “moving target group averaging
approach” of the cortex-based alignment (CBA) procedure
(Fischl et al., 1999; Goebel et al., 2006). Anatomical masks
of the temporal lobe and the primary auditory cortex were
manually drawn in the common CBA space based on
(Baumann et al., 2013; Bonte et al., 2013; Kim et al., 2000;
see Fig. 2A).
BOLD and CBF Time Courses
The BOLD and CBF time courses were calculated from
the motion corrected ASL time course using the
BrainVoyager ASL Perfusion Volume Data Processing
plugin performing surround averaging and subtraction:
The ASL time course is separated in a control and label
time-series. Each time-series is temporally interpolated to
obtain BOLD and CBF data points at each original TR. The
subtraction of the interpolated label from the interpolated
control time-series yields the CBF time course, while the
addition of the two interpolated time-series yields the
BOLD time course (Liu and Wong, 2005).
Both BOLD and CBF time-series were transformed from
the functional space to the anatomical space of each individual subject by applying the coregistration transformation previously calculated and the data was slightly
upsampled to an isotropic resolution of 2mm (i.e., half the
anatomical resolution). Finally, both time-series were spatially smoothed in the volume space with a 2mm FWHM
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3D Gaussian kernel (Gardumi et al., 2016) and temporally
high-pass filtered removing linear and low frequency nonlinear drifts up to 3 cycles per time course.
In order to evaluate the signal quality of the CBF and
BOLD time-series, two measures of signal-to-noise ratio
(SNR) were employed: the temporal signal-to-noise ratio
(tSNR) and the contrast-to-noise ratio (CNR). The tSNR
was calculated as the mean of the time course divided by
its standard deviation and averaged across all the voxels
in the temporal mask of AC (see Fig. 2A). The CNR, which
represents a quantity more closely linked to the functional
sensitivity of the data, was calculated as the ratio between
the standard deviation of the activation response and the
standard deviation of the noise (Welvaert and Rosseel,
2013). In the CNR calculation, only active (as defined via a
GLM analysis, see following section) voxels were included.
The CNR was computed for all center frequencies together
and for each of them separately. A two-way repeated measure ANOVA, performed with contrast (CBF or BOLD signal) and center frequency (i.e., the eight stimulus
conditions) as factors and a significance level a of 0.05,
was used to assess differences in CNR.
In order to investigate the consistency of CBF and
BOLD signal changes across subjects, we computed the
coefficient-of-variation (COV), defined as the standard
deviation divided by the mean of the percent signal
change across the subjects and expressed in percentage.
For each subject, the percent signal change was calculated
as the ratio between the amplitude of the mean evoked
response and the temporal mean of the time course using
the same number of active voxels for CBF and BOLD
signal.
Computation of the BOLD- and CBF-Based
Tonotopic Maps
BOLD- and CBF-based tonotopic maps were independently calculated following the same two-step procedure:
First, a general linear model (GLM) analysis was performed using one predictor for each of the eight stimulus
conditions (i.e., each of the eight center frequencies) to
model the respective BOLD/CBF response. The predictors
were built convolving a box car function representing the
stimulus block with a canonical (double gamma) hemodynamic response function (HRF) in order to account for the
hemodynamic response delay. For each run, one constant
predictor and the six parameters estimated by the motion
correction algorithm were included in the GLM as confound predictors. The obtained BOLD/CBF statistical
maps were thresholded (uncorrected t-value > 2 for the
contrast: all center frequencies > baseline) to select only
active voxels entering the 2nd step.
Second, for each active voxel, its BOLD signal/CBF preferred frequency was defined as the one having the highest b-value among the BOLD signal/CBF predictors. The
final single-subject BOLD signal/CBF tonotopy was
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Tonotopic Maps in Auditory Cortex Using ASL
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in the auditory cortex and its spatial consistency across
subjects, probabilistic maps were computed by calculating
at each vertex of the common CBA space the relative number of subjects having significant activity at that spatial
location. For this analysis, a threshold of q(FDR)<0.05
(contrast: all center frequencies > baseline; FDR standing
for false discovery rate) was used. The difference in number of voxels significantly active for CBF and BOLD signal
was tested using a two-tailed t-test (with significance level
a of 0.05).
Comparison of BOLD- vs. CBF-Based Tonotopic
Maps
BOLD and CBF tonotopies were compared by calculating their spatial correlation at the single-subject level in
the native anatomical space of each subject and at group
level in the CBA surface space. The significance of the correlation at single-subject level was evaluated by estimating
the null-distribution via permutation test (N_perm 5 1000).
For each iteration of the permutation test, the spatial correlation was computed between the original BOLD tonotopy
and the permuted CBF tonotopy obtained by randomly
permuting the preferred frequencies across the voxels in
the tonotopic map. Here and in the rest of the paper,
unless specified, by the term correlation, we refer to Pearson’s correlation calculated between the two maps represented in vectors.
Baseline CBF Quantification
Figure 2.
Anatomical masks and probabilistic maps of activation. Panel A
shows the anatomically defined temporal (in light blue) and primary auditory cortex (in pink, overlapped on it) masks. Panel B
shows the probabilistic map (from 10 to 100% overlap across
subjects) of activation in response to tones. Activation maps
from the CBF time-series are shown in red (top row), while
those from the BOLD time-series in blue (bottom row). [Color
figure can be viewed at wileyonlinelibrary.com]
obtained by color-coding each voxel according to its
preferred frequency: with red-to-green-to-blue coding for
low-to-medium-to-high frequencies taking into account the
logarithmic spacing of frequency in the stimuli. For visualization, the maps were projected on the inflated cortical
surfaces. The color-coded single-subject maps were transformed into the common CBA space and averaged to
obtain a group tonotopic map (Da Costa et al., 2011; De
Martino et al., 2013; Formisano et al., 2003; Herdener et al.,
2013; Humphries et al., 2010; Langers et al., 2007; Moerel
et al., 2012).
Results from the GLM analysis performed as first step
of the tonotopy computation were used also to assess the
overall response to the sound stimuli as measured by CBF
and BOLD signal. To evaluate the extension of activation
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A quantitative perfusion (CBF) map was estimated from
the ASL data using the model proposed by the ASL white
paper (Alsop et al., 2015). The assumption of this model
are: complete bolus delivery to the target tissue (i.e.,
PLD > ATT for pCASL, where ATT stands for arterial transit time); no venous outflow of labeled blood water (which
is generally valid in humans); and T1 relaxation time of
labeled spins to be the same as T1 of blood (at 3T, these
values are similar and therefore errors are negligible (Cavusoglu et al., 2009)). Given that the assumptions are satisfied in our study, quantitative CBF was calculated in each
voxel using the following equation:
PLD
6000 k b eT1blood
½ml=100g=min
qCBF5
2 s
2 a T1blood M0 12e T1blood
(1)
where b is obtained by estimating the full GLM ASL model (for details see Hernandez-Garcia et al., 2010; Mumford
et al., 2006). In this model, b is a scaling parameter of the
baseline CBF predictor (constructed as an alternation of
20.5 and 0.5) and is proportional to the label and control
signal difference. Since CBF and BOLD signal changes
related to activation are also modeled in this GLM
approach, the b estimate of baseline CBF is not biased by
signal changes induced by the task auditory stimulation.
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Other parameters in Eq. (1) are: M0 representing the signal
intensity of the M0 image; PLD the post labeling delay
(adjusted for each slice according to the slice acquisition
order); s the label duration; T1blood the longitudinal relaxation time of blood in seconds (T1blood 51:650s at 3T; Lu
et al., 2004; Zhang et al., 2013); k the brain/blood partition
coefficient in ml/g (k50:9ml=g); and a the labeling efficiency (a50:85 for pCASL). Finally, the factor 6000 converts the units from 1/s to ml/100g/min, which is the
commonly used physiological unit for quantitative CBF.
The resulting quantitative CBF map was used to calculate
single-subject gray matter (GM) perfusion values by averaging the CBF values across all voxels included in a GM mask
defined on the base of the individual anatomical segmentation and intersected with the ASL imaging slab. The group
perfusion map was obtained as the average of the singlesubject perfusion maps after registration in the CBA space.
Voice Selective Regions
The BOLD and CBF time-series calculated from the ASL
voice localizer run were projected on the individual surface space and, independently, analyzed with a GLM
including one predictor for each category (vocal sounds,
other natural sounds and tones). The predictor was built
as the convolution of the box car representing the stimulus
block with a canonical (double gamma) HRF. b estimates
of all predictors were calculated at each vertex of the cortical surfaces. Then, the individual b estimates were projected in the CBA surface space and a second level (i.e.,
multi-subject) GLM was performed taking into account the
variability across the subjects (random effects group analysis, RFX). The obtained CBF and BOLD statistical maps
were thresholded (uncorrected t-value > 2; contrast: all
voice localizer sounds > baseline) to select only vertices
showing stimulus-induced activity. Such vertices were
included in the computation of the contrast vocal
sounds > (other natural sounds 1 tones)/2. In this manner
CBF and BOLD unthresholded voice selective maps were
obtained. Their (dis)agreement was assessed computing
the correlation between the two maps. The approach presented here intentionally avoids to statistically threshold
the maps at significance level in order to compare CBF
and BOLD-based voice selective maps circumventing the
issue of lower SNR for CBF compared to BOLD signal.
Their comparison through correlation is a valid approach,
but unthresholded maps have to be interpreted with caution. In the Supporting Information, the comparison of
CBF and BOLD voice selective regions was performed also
using statistically thresholded maps, with qualitatively
similar results (see Supporting Information).
In order to quantitatively compare our results with previous findings using standard BOLD fMRI, we computed
the correlation between the BOLD unthresholded voice
selective map obtained in this study and that made available at http://neurovault.org/collections/33/ by Pernet
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et al. (2015). The correlation was calculated after transforming the latter from MNI space to our group-specific
CBA space and considering only the common vertices
between the two maps.
Vein Masks From Susceptibility Weighted Images
Vein masks were generated from SWI in order to assess
the tissue specificity of the BOLD and CBF activation signal. SWI takes advantage of the complementary information of T2* weighted magnitude and phase images
(Haacke et al., 2004; Rauscher et al., 2006; Reichenbach
et al., 1997). Because of their paramagnetic properties,
venous vessels look dark on magnitude images and take
negative values in the phase image. SWI uses the phase
information to further suppress the magnitude intensity of
venous vessels and therefore enhance their detection in
the final SW image. A sliding minimum intensity projection (mIP) over two subsequent slices of the SW image
was performed in order to profit from vessel connectivity
while preserving the local information across the slice
direction to the level of the resolution of the functional
maps (i.e., 2mm). Vein masks were created by binarising
the mIP-SW image with the value 1 assigned to voxels
having a SWI value lower than 1/5 of the maximum SWI
value and the value 0 otherwise. The vein mask was coregistered to the individual anatomy applying the coregistration parameters estimated between magnitude image of
the SWI and MP2RAGE image of the anatomy. Finally, the
coregistered vein mask was downsampled to an isotropic
resolution of 2.0mm to match the resolution of the functional data (Harmer et al., 2012). The resulting vein mask
(see Supporting Information Fig. S1) was visually
inspected by overlaying it on the original SW image and
the T1 weighted anatomy. Note that the obtained vein
mask may include CSF voxels given that also CSF signal is
suppressed in SWI images; however, this does not represent a concern for our analyses since we used SWI information only for voxels detected as active by GLM of CBF
and/or BOLD signal and therefore most likely consisting
of tissue, vessels and/or CSF containing vessels.
The fraction of active voxels, as previously defined by a
GLM analysis of the BOLD and CBF signal, labeled as
vein voxels in the vein mask was determined. Then, we
investigated whether the presence of a vein biasing more
strongly the BOLD signal than the CBF signal could
explain the partial mismatch between the BOLD and CBF
tonotopies. To that end, we calculated the correlation
between BOLD and CBF tonotopies splitting the voxels
according to the vein mask labeling.
RESULTS
Activation in Auditory Cortex
Figure 2A shows the two anatomically defined masks
used in this study: in light blue, the temporal cortex mask
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including the superior temporal plane (STP), STG, superior
temporal sulcus (STS) and middle temporal gyrus (MTG);
in pink the primary auditory cortex mask including HG and
the areas immediately anterior and posterior to it. Masks
were drawn in the common CBA space based on
(Baumann et al., 2013; Bonte et al., 2013; Kim et al., 2000).
Tones elicited a robust activation in the auditory cortex
as measured by both BOLD and CBF signals and shown in
Figure 2B by a probabilistic map of the contrast between
all stimulus conditions and baseline. The activated areas
included HG, planum temporale (PT), planum polare (PP),
STG, and STS. BOLD activation clusters were more widespread than CBF ones, as expected because of the lower
SNR of CBF signal compared to the BOLD signal. Thresholding each single-subject statistical map with
q(FDR)<0.05, the resulting number of active voxels was
significantly higher for the BOLD signal with respect to
CBF signal (2036 6 274 [range 414-3303] for BOLD signal
and 729 6 108 [range 140-1458] for CBF, reported as
mean 6 standard error across the subjects and [range minmax value]; P < 0.001, two-tailed t-test; see Supporting
Information Fig. S2 for a boxplot of the value distributions). The tSNR, calculated averaging across all the voxels
in the temporal cortex mask of AC, was 57.6 6 1.4 for
BOLD signal and 2.3 6 0.1 for CBF (where the tSNR values
are reported as mean 6 standard error calculated across the
subjects). More closely linked to the functional sensitivity
of the data, the CNR of the two time-series resulted in a
value of 0.206 6 0.014 for the BOLD signal and
0.130 6 0.004 for CBF (reported as mean 6 standard error
across the subjects). CNR was also calculated separately
for each center frequency and a two-way repeated measure ANOVA was performed with contrast (CBF or BOLD
signal) and center frequency (i.e., the eight stimulus conditions) as factors. Results showed a significant main effect
for the contrast (P < 0.001), but no significant effect for the
center frequency nor significant interaction between the
two factors.
The average percent signal change was 1.53 6 0.12% for
the BOLD signal and 16.50 6 1.19% for CBF signal
(reported as mean 6 standard error calculated across the
subjects), resulting in a COV of 27.73% and 24.99%,
respectively.
CBF and BOLD Tonotopies
Tonotopic maps for CBF and BOLD signal are shown in
Figure 3A (top and bottom row, respectively). The CBF
tonotopy presented two reversed spatial gradients of preferred frequencies located on HG: preferred low frequencies (in red) were located on the central part of HG and
preferred high frequency (in blue) medially and posteriorly to it, forming a gradient pattern of high-low-high frequency. Additional gradients were located in the
surrounding areas. More specifically, clusters of low frequencies were identifiable on the middle part of the STG
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lateral and anterior to HG, and on the posterior STG. The
tonotopic patterns are similar across the left and right
hemispheres. The overall layout and the spatial arrangement of the tonotopic gradients described for the CBF
tonotopy is in good (qualitative) agreement with those in
the BOLD tonotopy (in the corresponding panel of Fig.
3A, the two reversed gradients forming the high-low-high
frequency pattern are indicated by white double arrows
and the additional low frequency clusters by black single
arrows), and similar to maps shown in previously published BOLD signal studies (Da Costa et al., 2011; Formisano et al., 2003; Humphries et al., 2010; Moerel et al.,
2012). However, in the CBF tonotopy, extreme low or high
preferred frequency values are less represented than in the
BOLD tonotopy. A few limited mismatches between CBF
and BOLD signal tonotopy are highlighted in Figure 3A
by white single arrows.
The correlation between the CBF and BOLD tonotopic
maps in the individual volume space was significantly
above chance for all subjects but one (i.e., 11 out of 12 subjects; P < 0.01 as assessed by permutation test) resulting in
a mean correlation of 0.1560.06.
The spatial correlation between the BOLD and CBF
group tonotopic maps was calculated in the group-aligned
surface space and resulted in a value of 0.45. Such correlation increased to a value of 0.67 when restricting the computation to the vertices within an anatomically defined
mask of primary auditory cortex (PAC; see Fig. 2A, pink
mask). In this ROI, the right hemisphere showed a slightly
higher correlation value than the left one (0.72 and 0.65,
respectively).
Perfusion Map and Parcellation of the Primary
Auditory Cortex
A GM baseline perfusion value of 54 6 2 ml/100g/min
was obtained as mean and standard error across the subjects. Supporting Information Figure S3 shows a multi-slice
view of the quantitative perfusion map for each subject.
Figure 3B shows the group perfusion map, the histograms
of the GM perfusion values at vertex level in the left and
right hemisphere, and a zoomed view of the temporal
lobes after thresholding (value > 68 ml/100g/min) in order
to highlight the region(s) with higher baseline perfusion.
(Note that due to the limited brain coverage of the ASL
acquisition, there are no CBF values detected for the top
part of the cortex.) In left and right AC, a relatively homogeneous region with high perfusion was centered on HG
and extended posteriorly and medially to it. These areas
corresponded in both hemispheres to the main high-lowhigh frequency pattern observed in the previously computed tonotopic maps and to the presumed location of the
PAC. The correspondence is appreciable by contouring the
high perfusion region and superimposing such contour to
the tonotopy (Fig. 3A): the perfusion-based contour
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Figure 3.
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Tonotopic Maps in Auditory Cortex Using ASL
“segments” the V-shaped gradient of high-low-high frequency cutting through the low frequency region.
The overlap between this high perfusion region and the
anatomically defined PAC was 72.15% (Supporting Information Fig. S4).
Voice Selective Regions
Figure 4 shows the unthresholded voice selective maps
in the CBA surface space obtained from CBF and BOLD
time courses using RFX GLM and computing the contrast
vocal sounds > (other natural sounds 1 tones)/2. The overlaid black contours and the Supporting Information Figure
S5A show the voice selective regions defined as those
regions showing significantly higher activation to vocal
sounds compared to other natural sounds and tones.
BOLD signal voice selective regions presented several
peaks of voice sensitivity, and in particular, on mid STS
(lateral to HG), posterior STS and STG, and anterior STS
for the left hemisphere only. Outside the temporal lobe, a
significant cluster was detected bilaterally on the inferior
frontal gyrus by both CBF and BOLD voice sensitive mapping (see Supporting Information Figs. S6 and S7). This
configuration is in agreement with previous studies,
although an additional cluster on anterior STS in the right
hemisphere is sometimes found (Belin et al., 2000; Bonte
et al., 2013, 2014; Moerel et al., 2012; Pernet et al., 2015). In
contrast, the extension of CBF voice selective regions was
very limited, probably due to the lower CNR as suggested
by below-threshold effects (see Supporting Information
Fig. S5B and section “Voice selective regions for a large
range of initial vertex-level threshold”). To overcome the
limitation of low SNR for CBF, the agreement between
CBF and BOLD signal voice selectivity was assessed calculating the Pearson’s correlation between the two corresponding unthresholded maps. A correlation of 0.3815
(P < 0.001, two-tailed t-test) was found. Finally, good
agreement was found between the BOLD signal voice
selective map obtained in this study with that obtained by
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Pernet et al. (2015) using standard BOLD fMRI (Pearson’s
correlation of 0.4810; P < 0.001, two-tailed t-test).
Vein Masks From Susceptibility Weighted Images
We found no significant difference in the fraction of
vein voxels when considering the whole ROI of BOLD versus CBF active voxels. However, considering the active
voxels non-overlapping between the BOLD and the CBF
active ROIs, we found a significantly higher fraction of
vein voxels for BOLD exclusively active voxels versus CBF
exclusively active voxels (35% 6 2% and 27% 6 2%, respectively; t-test(11) 5 3.6114; P 5 0.0041).
Hypothesizing a relationship between vein biasing and
BOLD-CBF tonotopy mismatch, we calculated the correlation between BOLD and CBF tonotopy splitting the voxels
according to the vein mask labeling. Vein voxels had a
BOLD-CBF tonotopy correlation of 0.142 6 0.039 and nonvein voxels of 0.163 6 0.046 resulting in a non-significant
difference (t-test(11) 5 0.5169; P 5 0.3077).
DISCUSSION
The present study investigated the tonotopic organization of the human auditory cortex using ASL fMRI. In
contrast to standard BOLD fMRI, ASL allows to simultaneously measuring CBF and BOLD signal. CBF has the
advantage of being quantitative and physiologically meaningful, having higher spatial specificity and reproducibility, albeit with lower SNR compared to the BOLD signal.
ASL has been previously employed to map the topography of a sensory system, namely retinotopy in visual cortex (Cavusoglu et al., 2012), while the current study, to the
best of our knowledge, is the first to employ the ASL technique to perform tonotopic mapping in the auditory
cortex.
As expected, the passive listening of the stimulus tones
activated the auditory cortex bilaterally in a wide range of
areas, such as HG, PT, PP, STG and STS. The extent of the
Figure 3.
Group tonotopy (A) and quantitative baseline perfusion (B)
maps. Panel A shows the group tonotopy obtained from CBF
(top row) and the BOLD signal (bottom row). Overlaid to the
right BOLD map, two white double arrows show the main gradients composing the V-shaped frequency pattern of PAC and
the black single arrows show clusters of low frequencies, which
might belong to additional gradients outside PAC. Given the
symmetry between hemispheres and the good agreement
between CBF and BOLD tonotopy, such guiding symbols are
presented only in one panel. In both CBF and BOLD tonotopic
maps, regions presenting a mismatch between the two maps are
indicated by white single arrows. Panel B shows the group quantitative perfusion map and the histograms of the perfusion values
separately for the two hemispheres (LH and RH stand for left
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and right hemisphere, respectively). In the plots, the green bar
indicates the threshold of 68 ml/100g/min (corresponding to the
80th percentile of the baseline perfusion in the temporal mask).
The white rectangle overlaid to the hemispheres shows the
region of the close-up view used for Panel A and the inserts in
the histogram plots below. The inserts show a close-up view of
the thresholded perfusion map (CBF > 68 ml/100g/min; masked
with the anatomically defined temporal mask (Figure 2A, blue
mask) and 25 mm2 cluster size threshold), in which one can
observe a homogeneous high perfusion region centered on HG.
The contour of this bilateral region of high perfusion in AC was
outlined in black on all other maps. [Color figure can be viewed
at wileyonlinelibrary.com]
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Figure 4.
Unthresholded voice selective maps. Unthresholded CBF and
BOLD voice selective maps (top and bottom row, respectively)
were obtained using an RFX multi-subject GLM and computing
the contrast vocal sounds > (other natural sounds 1 tones)/2 for
the vertices which showed stimulus-induced activity for both
CBF and BOLD signal (uncorrected t-value > 2; contrast: all
voice localizer sounds > baseline). The black contours indicate
the voice selective regions obtained in the statistically thresholded maps computed in the Supporting Information (see Supporting Information Fig. S5). [Color figure can be viewed at
wileyonlinelibrary.com]
activated areas was significantly larger when estimated
from the BOLD signal than from CBF. Such difference was
primarily most likely due to the different inherent SNR of
the two contrasts, which is (for typical 3T human imaging
parameters) three to five times lower for CBF than for
BOLD signal (Cavusoglu et al., 2012). In the current study,
CNR of the BOLD signal was approximately twice as large
as the CNR of CBF. These numbers are in agreement with
the number of voxels detected as significantly active in
this study. Despite the lower SNR, we observed a lower
coefficient of variation for CBF percent signal change compared to BOLD, implying a higher reproducibility of CBF
values, in agreement with previous studies (Leontiev and
Buxton, 2007; Tjandra et al., 2005).
We demonstrated the feasibility of tonotopic mapping
using CBF signal measured with ASL technique, specifically pCASL at 3T. The CBF tonotopy clearly showed a
main V-shaped gradient of high frequencies around a
low frequency region centered on HG and additional gradients in surrounding areas. The overall pattern of the
tonotopic map was similar across the two hemispheres.
The CBF tonotopy was in good agreement with the
BOLD tonotopy obtained by the BOLD time course
extracted from the same ASL signal. The BOLD tonotopy
obtained from the ASL sequence, in turn, agreed very
well with those of previous studies employing GE-EPI
BOLD sequences (Da Costa et al., 2011; Formisano et al.,
2003; Humphries et al., 2010; Moerel et al., 2012; Saenz
and Langers, 2014). Although acquired simultaneously
and therefore susceptible to the same correlated artifacts
(e.g., physical noise, motion, . . .), ideally, the BOLD signal and CBF represent physically independent modulation of the ASL signal. That is, the presence of tonotopy
in both the BOLD signal and CBF provides reciprocal validation of the utility of both parameters for probing the
human AC.
Despite the good correspondence between the spatial
locations of the gradients between BOLD and CBF tonotopy, we observed a less steep gradient between the two
extremes of the frequency scale using CBF, resulting in
smaller areas activated preferentially by the lowest or
highest frequencies. We attribute this discrepancy to the
inherently lower SNR of CBF resulting in noisier singlesubject maps and therefore favoring intermediate
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Tonotopic Maps in Auditory Cortex Using ASL
frequency range due to the averaging of best frequency
values involved in the calculation of group maps.
To focus our analysis on the auditory core, we defined a
mask including HG and the areas immediately surrounding
HG anteriorly and posteriorly (see Fig. 2A). The mask was
anatomically defined in the CBA group space on the basis
of previous literature and current best practice (Humphries
et al., 2010; Langers and van Dijk, 2012; Moerel et al., 2014).
The rationale of focusing on the auditory core is that this is
the area of AC that, despite the debate about the orientation
of PAC, is most consistently described and reliably interpreted across different studies at different field strength,
using different stimuli and at single-subject and group level
(Moerel et al., 2014). Interestingly, restricting the analysis to
the anatomically defined PAC increased the spatial correlation between BOLD and CBF group tonotopic maps. This
result confirms that human tonotopic maps–as obtained
with tones–are more reliable and more consistent across
subjects in PAC than in the whole AC. In future studies, it
would be interesting to see whether combining CBF-based
tonotopic mapping and natural sounds, which engage the
whole auditory cortex in an ecologically valid manner
(Moerel et al., 2012), increases consistency also outside
PAC.
Additionally to the functional ASL runs, an M0 image
was acquired to allow the quantification of brain perfusion. On the basis of the quantification formula proposed
by the ASL white paper (Equation (1); Alsop et al., 2015),
we estimated the quantitative baseline CBF voxel-by-voxel
using the full ASL model to obtain a perfusion estimate
unbiased by the auditory activation due to the tone presentation. In the auditory cortex, we observed a region
characterized by higher baseline perfusion values in the
location of HG and immediate surroundings for both
hemispheres. This finding is in agreement with the values
of regional CBF reported by (Chen et al., 2011; cfr. Table 2
“Transverse temporal – young adults” and Fig. 2),
although in the cited paper no specific comment was done
on this regard. At least two alternative explanations of the
observed higher CBF in the presumed auditory core are
possible: One possible cause is the noise of the MR gradients during image acquisition. Alternatively, relatively
higher CBF is due to higher vascularization in the auditory
core and thus independent of the MRI acquisition. In other
words, even though the MR gradient noise is a stimulus
for the auditory cortex, the spatial distribution of relative
CBF can be preserved under the MRI conditions. Regardless of the underlying cause, we suggest that such high
localized perfusion area detected bilaterally in the auditory
cortex identifies the primary auditory core (the homologues of monkey areas A1 and R). This interpretation is
supported by previous findings that primary (visual, auditory, and somatosensory) areas have higher vascular density and steady-state metabolic demands than secondary
areas (Weber et al., 2008), and by the correspondence
between the location of the high perfusion area and that of
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the main high-low-high frequency gradients of both BOLD
and CBF tonotopies. Interestingly, the anterior border of
the high perfusion area cuts through the low preferred frequency area of the gradient. This offers a possible distinction between primary and non-primary auditory regions
otherwise not possible on the basis of the tonotopic information alone. In conclusion, independently from its cause,
we suggest that the observed higher perfusion is spatially
restricted to early auditory areas, thus, allowing the parcellation of the auditory cortex.
We investigated the tissue specificity of BOLD and CBF
signals and found a significantly higher number of vein
voxels among BOLD signal active voxels, compared to
when using CBF. Vein voxels were defined using vein
masks created from SWI images optimized to enhance
venous vessels from the surrounding tissue. Our results
are in agreement with previous studies reporting a venous
bias of the BOLD signal whilst a higher specificity to the
capillary beds for CBF signal (Aguirre et al., 2002; Tjandra
et al., 2005; Wang et al., 2003). We hypothesized that such
venous bias of the BOLD signal could explain the
observed BOLD signal-CBF tonotopy mismatches, but no
significance difference was found between the BOLD
signal-CBF tonotopy correlations of vein versus non-vein
voxels. Further investigations are needed to shed light on
the origin(s) of the observed mismatches between BOLD
signal- and CBF-based tonotopy possibly using higher spatial resolution reducing partial volume effects between tissue, veins and CSF, in particular outside of the PAC.
To further characterize the human auditory cortex, we
investigated a higher order functional property such as
voice sensitivity. Voice selective regions were investigated
by contrasting responses to vocal sounds versus those to
other natural sounds and tones as measured by CBF and
BOLD signal computed from the ASL signal of the voice
localizer run. BOLD signal defined voice selective regions
were mainly located on STG and STS and presented five
peaks of voice sensitivity, namely posterior and mid STS
for both hemispheres and anterior STS for the left hemisphere in good agreement with previous studies (Belin
et al., 2000; Bonte et al., 2013, 2014; Moerel et al., 2012; Pernet et al., 2015). CBF defined voice selective regions,
although showing a more limited extent, successfully
detected three peaks corresponding to the bilateral posterior and the left mid STS clusters. The correlation analysis
between the unthresholded BOLD- and CBF-based voice
selective maps showed their relatively good agreement
and further support the hypothesis that differences in
extent and number of detected peaks was most likely due
to the different CNR of the BOLD and CBF signal.
Limitations and Benefits of Tonotopy Using ASL
The most stringent limitation of using CBF signal is its
low SNR (compared to the standard BOLD signal). In this
study, we assessed the CNR as a measure of the functional
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sensitivity of the data and we found a CNR 1.6 times lower for CBF than BOLD signal. Thus, differences between
auditory processing as detected using CBF and BOLD signal can either be attributed to the differences in the biophysical origins of both signals or to differences in their
CNRs. In future studies, an adequately larger number of
trials could be used to overcome the ASL CNR penalty.
Moreover, the labeling duration and PLD used in this
study were shorter than those recommended by Alsop
et al. (2015). Using a pCASL sequence with longer labeling
duration and PLD might have resulted in higher SNR of
the baseline CBF. Note, however, even though some of the
quantitative results on image SNR and tSNR of CBF are
affected by the choice of ASL parameters, the results on
CBF tonotopic maps and their comparison with BOLD signal tonotopic maps are qualitatively insensitive for a wide
range of these parameters.
Another limitation of using ASL techniques is the need
of acquiring tag-control pairs of images, which results in
an effective temporal resolution lower than the nominal
TR. Moreover, the TR itself cannot be as short as in BOLD
imaging because of the post-labeling delay to allow the
blood to reach the imaging slab. Such transit time constitutes a time constraint of the order of 700-2800 ms
depending of the region of interest (Mildner et al., 2014),
which, however, enables presenting the auditory stimuli
within the silent period of the delay.
On the other hand, CBF offers some important advantages such as quantification, physiological unit of measure,
reproducibility and spatial specificity. Moreover, our results
show that the baseline perfusion signal offers additional
information to characterize AC. Most importantly, delineating the primary auditory core on the basis of the perfusion
baseline map alone provides complementary and independent information to anatomical landmarks or myelin delineations. ASL perfusion baseline measurements can be
performed without sound presentation and with a run
duration of 3–10 minutes (depending on the spatial resolution), therefore in a much shorter acquisition time than usual tonotopy protocols. FMRI studies interested in PAC
localization, but not in tonotopic information per se, could
therefore greatly benefit from perfusion baseline PAC delineation as they could invest the spared time in the effect/
task of interest. In addition, differences in baseline perfusion between populations (e.g., healthy subjects vs Tinnitus
patients) may be detected and be meaningful in characterizing the state of the auditory processing in these populations. Furthermore, venous biases potentially confounding
BOLD signal maps (such as detected in V4 in the visual
cortex, see Winawer et al., 2010) may be absent in CBF
maps, which therefore may yield a more faithful representation of the underlying neuronal functional architecture.
CONCLUSIONS
In this study, we demonstrated the feasibility of tonotopy and voice area mapping in human auditory cortex
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using CBF obtained with an ASL MRI sequence. We
described the limitations and benefits of this approach
compared to standard BOLD fMRI: CBF is characterized
by a lower CNR and temporal resolution, but is a quantifiable physiological measure, has higher reproducibility,
higher spatial specificity, and ASL sequences allow the
simultaneous acquisition of CBF and BOLD signal and
sound presentation during the silent PLD. Interpreting the
perfusion baseline map and tonotopy together, we propose
quantitative baseline perfusion as a novel marker to identify the primary auditory cortex.
ACKNOWLEDGMENTS
The authors thank Federico De Martino for the implementation of the auditory stimuli and helpful discussions.
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